Over 600,000 people in the US live with major lower limb loss, and the prevalence of limb amputation is expected to double by 2050. Many amputees rely on lower limb prostheses to regain some function of the missing limb, though their mobility, stability, and community participation remain substantially limited. Compared to traditional energetically-passive devices, modern powered knee prostheses promise to restore more natural locomotion and provide greater functionality. Most powered knee prostheses rely on finite state impedance control (IC), which adjusts the impedance of the knee joints based on gait phase. The desired IC parameter values in each gait phase are often fine-tuned manually and heuristically by a prosthetist, based on observations of the patient's gait performance and feedback, until the amputee's gait “looks good”. In other words, the IC parameters are manually fine-tuned based on qualitative observations of the subject according to conventional techniques. Manual tuning presents a serious clinical challenge since it lacks precision, is time and resource intensive, and must be conducted uniquely for each amputee to account for between-patient variation. New approaches that can configure the prosthesis control parameters quickly and cost-effectively are needed to make powered lower limb prostheses more practical for clinical use.
The two main concepts to simplify the tuning procedure have been 1) to mimic able-bodied or sound limb impedance at prosthetic joints, and 2) to reduce the number of parameters that need to be tuned. Biological joint impedances have been computed directly from experimental measurements and estimated using biomechanical models. Due to experimental limitations, in vivo joint impedance during ambulation has only been measured at the ankle during the stance portion of gait. Impedance measurements at other joints, such as the knee, were made under static or quasi-static conditions and therefore may not transfer to dynamic ambulation tasks. Impedance estimated from musculoskeletal biomechanical models has only been validated for the stance phase of gait. Given the limited availability of biological impedance data, applying it toward the control of prosthetic joints during ambulation has not yet been demonstrated.
In a finite state machine-based controller, reducing the number of control parameters that must be calibrated may be achieved by defining fewer states. However, only modest simplification can be achieved since at least 3 states are typically defined for level-ground walking, and parameter values differ across tasks (e.g. ramp ascent/descent, stair ascent/descent). Another solution to tune fewer parameters is to associate parameter values with one another or with other intrinsic biomechanical measures (e.g. prosthesis joint angles, prosthesis load, walking speed, foot center of pressure, effective leg shape). In one case, this strategy not only reduced the burden of manual tuning, but also permitted alternate nonlinear control systems that had fewer control parameters altogether. However, given their complexity, explicit relationships between biomechanical measures and control parameters may be imprecisely known and potentially unsuitable as a basis for prosthesis control. Ultimately, parameter reduction only simplifies the tuning procedure, leaving many of the practical costs and challenges of tuning powered prostheses unsolved.